A poorly conceived data project can turn into a sinkhole for time, effort and money. To show why it’s so crucial to take a business-focused approach to data projects, here’s a business case from my experience as a data science advisor.
This scenario will sound familiar to anyone who’s tried to build a marketing automation system, or has transitioned a traditional marketing group from research-based targeted marketing to localized database marketing.
My data analytics company was acquired by an email marketing firm that was trying to become a data-driven marketing company. Shortly after, we started transitioning from a services/agency model to a full product company. Part of that plan was to build a targeted marketing automation engine.
I joined the project after it was already in progress. The customers with whom we chose to build a pilot program were a few large chain restaurants. They did mostly TV advertising and had started doing digital ads, but they were not yet effectively targeting. The first few customers would get a big benefit: a very customized approach to how the software and service would be set up to suit their needs.
The CMO (and project sponsor) at the first client was a seasoned researcher and very proud of their segmentation work based on custom psychographic clustering work. The mandate to our team was to take the clusters they were using for TV advertising and use those as the basis for the one-to-one digital targeting program. This would allow them to align their traditional campaigns with their digital campaigns.
Nice and easy, right?
The project team took this mandate literally and dove straight in. The data team’s objective became dissecting the composition of existing psychographic clusters and assigning each of the 2 million members in the client’s customer database to a cluster. This would enable the client to market to the individuals based on the previously determined media segments.
Because psychographic clusters are proprietary, this was no an easy task, and it ended up being extremely expensive. Originally estimated at over $2 per record, the clusters were eventually brought down to under 30¢ per record. Still, with 2 million records, this was a major investment for the client in new specialty data purchases.
As anyone who has worked with psychographic clusters could have predicted, when the segment identifiers were applied to individual people in the database, the limitations of this approach became painfully evident. Serving up an ad on TV and getting some of the audience wrong is low risk. However, when you direct message the “wealthy suburban dads” cluster only to find that 5% are women, 17% are not fathers and 12% are below average income, etc…the errors are not forgiving.
What’s more, the kinds of campaigns that were meaningful for mass media translated poorly to one-to-one messaging campaigns, so even once the database was fully segmented per the directive, the marketing execution wasn’t useful.
The project was an expensive disaster. And the most painful part is that we actually had rich behavioral and preference data for most of the 2 million customers in the database, which told us about their prior eating patterns, where they lived, who they dined with, what occasions they celebrated at the restaurant, how old they were, and they were willing to tell us more.
Eventually the project and the product was realigned around the behavioral data, but the missteps could have been avoided if the business problem had been clearly identified and refined around the available data and budgets at the beginning. The real business goal was to target customers using the best available—and most actionable—data in a way that would be most likely to alter their behavior in the company’s favor (e.g., increase purchase frequency and/or the check average).
If the project team had implemented the Pragmatic Data Analysis Model (also referred to as The Pragmatic Data Insights Model) and asked the right questions from the outset, they may have avoided the pitfalls of a business leader who confused research acumen for data/analytics understanding, and a data/analysis team that was more interested in diving in than really understanding the intricacies of the business problem they were trying to solve.
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